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8d070187e3
Fixes #129053 Previously interpolate had a bad signature and not correct type hints. This fixes this issue. Pull Request resolved: https://github.com/pytorch/pytorch/pull/157202 Approved by: https://github.com/ezyang, https://github.com/albanD
140 lines
3.8 KiB
Python
140 lines
3.8 KiB
Python
# ${generated_comment}
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# mypy: disable-error-code="type-arg"
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from collections.abc import Sequence
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from typing import Literal, overload
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from torch import memory_format, Tensor
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from torch.types import _bool, _device, _dtype, _int, _size
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# Defined in tools/autograd/templates/python_nn_functions.cpp
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${c_nn_function_hints}
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# Defined in aten/src/ATen/native/mkldnn/Linear.cpp
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def mkldnn_linear(input: Tensor, weight: Tensor, bias: Tensor | None) -> Tensor: ...
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# Defined at aten/src/ATen/native/mkldnn/MKLDNNConversions.cpp
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def mkldnn_reorder_conv2d_weight(
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self: Tensor,
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padding: list,
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stride: list,
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dilatation: list,
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groups: int,
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) -> Tensor: ...
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def mkldnn_reorder_conv3d_weight(
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self: Tensor,
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padding: list,
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stride: list,
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dilatation: list,
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groups: int,
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) -> Tensor: ...
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# Defined in aten/src/ATen/native/mkldnn/Prelu.cpp
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def mkldnn_prelu(input: Tensor, weight: Tensor) -> Tensor: ...
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# Defined at tools/autograd/templates/python_nn_functions.cpp
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@overload
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def _parse_to(
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device: _device,
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dtype: _dtype,
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non_blocking: _bool,
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copy: _bool,
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*,
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memory_format: memory_format,
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) -> tuple[_device, _dtype, _bool, memory_format]: ...
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@overload
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def _parse_to(
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dtype: _dtype,
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non_blocking: _bool,
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copy: _bool,
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*,
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memory_format: memory_format,
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) -> tuple[_device, _dtype, _bool, memory_format]: ...
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@overload
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def _parse_to(
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tensor: Tensor,
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non_blocking: _bool,
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copy: _bool,
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*,
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memory_format: memory_format,
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) -> tuple[_device, _dtype, _bool, memory_format]: ...
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# Defined in aten/src/ATen/native/PackedSequence.cpp
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def pad_sequence(
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sequences: list[Tensor] | tuple[Tensor, ...],
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batch_first: bool = False,
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padding_value: float = 0.0,
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padding_side: Literal["left", "right"] = "right",
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) -> Tensor: ...
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# Upsample functions used by torch.nn.functional.interpolate
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def upsample_nearest1d(
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input: Tensor,
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output_size: Sequence[int] | None,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def upsample_nearest2d(
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input: Tensor,
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output_size: Sequence[int] | None,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def upsample_nearest3d(
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input: Tensor,
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output_size: Sequence[int] | None,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def _upsample_nearest_exact1d(
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input: Tensor,
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output_size: Sequence[int] | None,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def _upsample_nearest_exact2d(
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input: Tensor,
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output_size: Sequence[int] | None,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def _upsample_nearest_exact3d(
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input: Tensor,
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output_size: Sequence[int] | None,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def upsample_linear1d(
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input: Tensor,
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output_size: Sequence[int] | None,
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align_corners: bool,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def _upsample_bilinear2d_aa(
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input: Tensor,
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output_size: Sequence[int] | None,
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align_corners: bool,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def upsample_bilinear2d(
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input: Tensor,
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output_size: Sequence[int] | None,
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align_corners: bool,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def upsample_trilinear3d(
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input: Tensor,
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output_size: Sequence[int] | None,
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align_corners: bool,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def _upsample_bicubic2d_aa(
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input: Tensor,
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output_size: Sequence[int] | None,
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align_corners: bool,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def upsample_bicubic2d(
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input: Tensor,
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output_size: Sequence[int] | None,
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align_corners: bool,
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scale_factors: Sequence[float] | None,
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) -> Tensor: ...
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def flatten_dense_tensors(tensors: list[Tensor]) -> Tensor: ...
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def unflatten_dense_tensors(flat: Tensor, tensors: list[Tensor]) -> list[Tensor]: ...
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